Data – preparing the fuel for LLMs
Preparing datasets for the effective training of LLMs is a multi-step process that requires careful planning and execution. Here is a comprehensive guide on how to prepare datasets.
Data collection
Data collection is a fundamental step in the development of LLMs and involves gathering a vast and varied set of text data that the model will use to learn. The quality and diversity of this corpus are critical as they directly influence the model’s ability to understand and generate language across different domains and styles. Let’s take a look at an expanded view of the data collection process:
- Scope of corpus: The corpus should cover a wide range of topics to prevent the model from developing a narrow understanding of language. It should include literature from various genres, informative articles from different fields, dialogues from conversational datasets, technical documents, and other relevant text sources. ...
